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How important is the input data for a ML model?


Predicting Soccer: guessing which matches a model will predict correclyBest regression model to use for sales predictionPython: How to make model predict in a generalized manner using ML AlgorithmLogistic regression on biased dataCategorizing Customer EmailsHow to compensate for class imbalance in prediction model?how to build a predictive model without training data neither historical dataMachine Learning in real timeWhat Machine Learning Algorithm could I use to determine some measure in a date?Can this be a case of multi-class skewness?













0












$begingroup$


Last 4-6 weeks, I have been learning and working for the first time on ML. Reading blogs, articles, documentations, etc. and practising. Have asked lot of questions here on Stack Overflow as well.



While I have got some amount of hands-on experience, but still got a very basic doubt (confusion) --
When I take my input data set with 1000 records, the model prediction accuracy is say 75%. When I keep 50000 records, the model accuracy is 65%.



1) Does that mean the model responds completely based on the i/p data being fed into?



2) If #1 is true, then in real-world where we don't have control on input data, how will the model work?



Ex. For suggesting products to a customer, the input data to the model would be the past customer buying experiences. As the quantity of input data increases, the prediction accuracy will increase or decrease?



Please let me know if I need to add further details to my question.



Thanks.



Edit - 1 - Below added frequency distribution of my input data:



enter image description here










share|improve this question











$endgroup$
















    0












    $begingroup$


    Last 4-6 weeks, I have been learning and working for the first time on ML. Reading blogs, articles, documentations, etc. and practising. Have asked lot of questions here on Stack Overflow as well.



    While I have got some amount of hands-on experience, but still got a very basic doubt (confusion) --
    When I take my input data set with 1000 records, the model prediction accuracy is say 75%. When I keep 50000 records, the model accuracy is 65%.



    1) Does that mean the model responds completely based on the i/p data being fed into?



    2) If #1 is true, then in real-world where we don't have control on input data, how will the model work?



    Ex. For suggesting products to a customer, the input data to the model would be the past customer buying experiences. As the quantity of input data increases, the prediction accuracy will increase or decrease?



    Please let me know if I need to add further details to my question.



    Thanks.



    Edit - 1 - Below added frequency distribution of my input data:



    enter image description here










    share|improve this question











    $endgroup$














      0












      0








      0





      $begingroup$


      Last 4-6 weeks, I have been learning and working for the first time on ML. Reading blogs, articles, documentations, etc. and practising. Have asked lot of questions here on Stack Overflow as well.



      While I have got some amount of hands-on experience, but still got a very basic doubt (confusion) --
      When I take my input data set with 1000 records, the model prediction accuracy is say 75%. When I keep 50000 records, the model accuracy is 65%.



      1) Does that mean the model responds completely based on the i/p data being fed into?



      2) If #1 is true, then in real-world where we don't have control on input data, how will the model work?



      Ex. For suggesting products to a customer, the input data to the model would be the past customer buying experiences. As the quantity of input data increases, the prediction accuracy will increase or decrease?



      Please let me know if I need to add further details to my question.



      Thanks.



      Edit - 1 - Below added frequency distribution of my input data:



      enter image description here










      share|improve this question











      $endgroup$




      Last 4-6 weeks, I have been learning and working for the first time on ML. Reading blogs, articles, documentations, etc. and practising. Have asked lot of questions here on Stack Overflow as well.



      While I have got some amount of hands-on experience, but still got a very basic doubt (confusion) --
      When I take my input data set with 1000 records, the model prediction accuracy is say 75%. When I keep 50000 records, the model accuracy is 65%.



      1) Does that mean the model responds completely based on the i/p data being fed into?



      2) If #1 is true, then in real-world where we don't have control on input data, how will the model work?



      Ex. For suggesting products to a customer, the input data to the model would be the past customer buying experiences. As the quantity of input data increases, the prediction accuracy will increase or decrease?



      Please let me know if I need to add further details to my question.



      Thanks.



      Edit - 1 - Below added frequency distribution of my input data:



      enter image description here







      machine-learning predictive-modeling machine-learning-model






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited 1 hour ago







      ranit.b

















      asked 2 hours ago









      ranit.branit.b

      427




      427




















          2 Answers
          2






          active

          oldest

          votes


















          0












          $begingroup$

          It looks like your model overfits did you try to do a train/test split?






          share|improve this answer









          $endgroup$












          • $begingroup$
            Thanks Robin. Yes, I've have a 75/25 split. Just out of curiosity, may I ask what hint made you think that the model overfits? ps. Added frequency distribution of my input data in the question.
            $endgroup$
            – ranit.b
            1 hour ago











          • $begingroup$
            So I guess it is your test accuracy which decreases. If your training accuracy keeps on increasing but your test accuracy decreases it meanss your model is overfitting.
            $endgroup$
            – Robin Nicole
            1 hour ago


















          0












          $begingroup$

          To answer your first question, the accuracy of the model highly depends on the "quality" of the input data. Basically, your training data should represent the same scenario as that of the final model deployment environment.



          There are two reasons why the scenario you mentioned is happening,



          1. When you added more data, maybe there is no good relationship between input features and label for the new examples. It is always said that less and clean data is better than large and messy data.



          2. If 49000 records added afterward are from the same set(i.e. have a good relationship between label and features) as that of 1000 before, there are again two possible reasons



            A. If accuracy on the train dataset is small along with test dataset. e.g. training accuracy is 70% and test accuracy is 65%, then you are underfitting data. Model is very complex and dataset is small in terms of the number of examples.



            B. If your training accuracy is near 100% and test accuracy is 65%, you are overfitting data. Model is complex, so you should go with some simple algorithm.



            NOTE* Since you haven't mentioned about training accuracy, it is difficult to say what out of two above is happening.



          Now coming to your second question about real-world deployment. There is something called model staleness over time which is basically the problem of reducing accuracy of a model over time. https://medium.com/thelaunchpad/how-to-protect-your-machine-learning-product-from-time-adversaries-and-itself-ff07727d6712, this is the article by a product manager at Google how staleness problem and how it can be solved. This will answer your second question.



          Let me know if something is not clear.






          share|improve this answer








          New contributor




          Sagar Shelke is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.






          $endgroup$












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            2 Answers
            2






            active

            oldest

            votes








            2 Answers
            2






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            0












            $begingroup$

            It looks like your model overfits did you try to do a train/test split?






            share|improve this answer









            $endgroup$












            • $begingroup$
              Thanks Robin. Yes, I've have a 75/25 split. Just out of curiosity, may I ask what hint made you think that the model overfits? ps. Added frequency distribution of my input data in the question.
              $endgroup$
              – ranit.b
              1 hour ago











            • $begingroup$
              So I guess it is your test accuracy which decreases. If your training accuracy keeps on increasing but your test accuracy decreases it meanss your model is overfitting.
              $endgroup$
              – Robin Nicole
              1 hour ago















            0












            $begingroup$

            It looks like your model overfits did you try to do a train/test split?






            share|improve this answer









            $endgroup$












            • $begingroup$
              Thanks Robin. Yes, I've have a 75/25 split. Just out of curiosity, may I ask what hint made you think that the model overfits? ps. Added frequency distribution of my input data in the question.
              $endgroup$
              – ranit.b
              1 hour ago











            • $begingroup$
              So I guess it is your test accuracy which decreases. If your training accuracy keeps on increasing but your test accuracy decreases it meanss your model is overfitting.
              $endgroup$
              – Robin Nicole
              1 hour ago













            0












            0








            0





            $begingroup$

            It looks like your model overfits did you try to do a train/test split?






            share|improve this answer









            $endgroup$



            It looks like your model overfits did you try to do a train/test split?







            share|improve this answer












            share|improve this answer



            share|improve this answer










            answered 1 hour ago









            Robin NicoleRobin Nicole

            3217




            3217











            • $begingroup$
              Thanks Robin. Yes, I've have a 75/25 split. Just out of curiosity, may I ask what hint made you think that the model overfits? ps. Added frequency distribution of my input data in the question.
              $endgroup$
              – ranit.b
              1 hour ago











            • $begingroup$
              So I guess it is your test accuracy which decreases. If your training accuracy keeps on increasing but your test accuracy decreases it meanss your model is overfitting.
              $endgroup$
              – Robin Nicole
              1 hour ago
















            • $begingroup$
              Thanks Robin. Yes, I've have a 75/25 split. Just out of curiosity, may I ask what hint made you think that the model overfits? ps. Added frequency distribution of my input data in the question.
              $endgroup$
              – ranit.b
              1 hour ago











            • $begingroup$
              So I guess it is your test accuracy which decreases. If your training accuracy keeps on increasing but your test accuracy decreases it meanss your model is overfitting.
              $endgroup$
              – Robin Nicole
              1 hour ago















            $begingroup$
            Thanks Robin. Yes, I've have a 75/25 split. Just out of curiosity, may I ask what hint made you think that the model overfits? ps. Added frequency distribution of my input data in the question.
            $endgroup$
            – ranit.b
            1 hour ago





            $begingroup$
            Thanks Robin. Yes, I've have a 75/25 split. Just out of curiosity, may I ask what hint made you think that the model overfits? ps. Added frequency distribution of my input data in the question.
            $endgroup$
            – ranit.b
            1 hour ago













            $begingroup$
            So I guess it is your test accuracy which decreases. If your training accuracy keeps on increasing but your test accuracy decreases it meanss your model is overfitting.
            $endgroup$
            – Robin Nicole
            1 hour ago




            $begingroup$
            So I guess it is your test accuracy which decreases. If your training accuracy keeps on increasing but your test accuracy decreases it meanss your model is overfitting.
            $endgroup$
            – Robin Nicole
            1 hour ago











            0












            $begingroup$

            To answer your first question, the accuracy of the model highly depends on the "quality" of the input data. Basically, your training data should represent the same scenario as that of the final model deployment environment.



            There are two reasons why the scenario you mentioned is happening,



            1. When you added more data, maybe there is no good relationship between input features and label for the new examples. It is always said that less and clean data is better than large and messy data.



            2. If 49000 records added afterward are from the same set(i.e. have a good relationship between label and features) as that of 1000 before, there are again two possible reasons



              A. If accuracy on the train dataset is small along with test dataset. e.g. training accuracy is 70% and test accuracy is 65%, then you are underfitting data. Model is very complex and dataset is small in terms of the number of examples.



              B. If your training accuracy is near 100% and test accuracy is 65%, you are overfitting data. Model is complex, so you should go with some simple algorithm.



              NOTE* Since you haven't mentioned about training accuracy, it is difficult to say what out of two above is happening.



            Now coming to your second question about real-world deployment. There is something called model staleness over time which is basically the problem of reducing accuracy of a model over time. https://medium.com/thelaunchpad/how-to-protect-your-machine-learning-product-from-time-adversaries-and-itself-ff07727d6712, this is the article by a product manager at Google how staleness problem and how it can be solved. This will answer your second question.



            Let me know if something is not clear.






            share|improve this answer








            New contributor




            Sagar Shelke is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
            Check out our Code of Conduct.






            $endgroup$

















              0












              $begingroup$

              To answer your first question, the accuracy of the model highly depends on the "quality" of the input data. Basically, your training data should represent the same scenario as that of the final model deployment environment.



              There are two reasons why the scenario you mentioned is happening,



              1. When you added more data, maybe there is no good relationship between input features and label for the new examples. It is always said that less and clean data is better than large and messy data.



              2. If 49000 records added afterward are from the same set(i.e. have a good relationship between label and features) as that of 1000 before, there are again two possible reasons



                A. If accuracy on the train dataset is small along with test dataset. e.g. training accuracy is 70% and test accuracy is 65%, then you are underfitting data. Model is very complex and dataset is small in terms of the number of examples.



                B. If your training accuracy is near 100% and test accuracy is 65%, you are overfitting data. Model is complex, so you should go with some simple algorithm.



                NOTE* Since you haven't mentioned about training accuracy, it is difficult to say what out of two above is happening.



              Now coming to your second question about real-world deployment. There is something called model staleness over time which is basically the problem of reducing accuracy of a model over time. https://medium.com/thelaunchpad/how-to-protect-your-machine-learning-product-from-time-adversaries-and-itself-ff07727d6712, this is the article by a product manager at Google how staleness problem and how it can be solved. This will answer your second question.



              Let me know if something is not clear.






              share|improve this answer








              New contributor




              Sagar Shelke is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
              Check out our Code of Conduct.






              $endgroup$















                0












                0








                0





                $begingroup$

                To answer your first question, the accuracy of the model highly depends on the "quality" of the input data. Basically, your training data should represent the same scenario as that of the final model deployment environment.



                There are two reasons why the scenario you mentioned is happening,



                1. When you added more data, maybe there is no good relationship between input features and label for the new examples. It is always said that less and clean data is better than large and messy data.



                2. If 49000 records added afterward are from the same set(i.e. have a good relationship between label and features) as that of 1000 before, there are again two possible reasons



                  A. If accuracy on the train dataset is small along with test dataset. e.g. training accuracy is 70% and test accuracy is 65%, then you are underfitting data. Model is very complex and dataset is small in terms of the number of examples.



                  B. If your training accuracy is near 100% and test accuracy is 65%, you are overfitting data. Model is complex, so you should go with some simple algorithm.



                  NOTE* Since you haven't mentioned about training accuracy, it is difficult to say what out of two above is happening.



                Now coming to your second question about real-world deployment. There is something called model staleness over time which is basically the problem of reducing accuracy of a model over time. https://medium.com/thelaunchpad/how-to-protect-your-machine-learning-product-from-time-adversaries-and-itself-ff07727d6712, this is the article by a product manager at Google how staleness problem and how it can be solved. This will answer your second question.



                Let me know if something is not clear.






                share|improve this answer








                New contributor




                Sagar Shelke is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.






                $endgroup$



                To answer your first question, the accuracy of the model highly depends on the "quality" of the input data. Basically, your training data should represent the same scenario as that of the final model deployment environment.



                There are two reasons why the scenario you mentioned is happening,



                1. When you added more data, maybe there is no good relationship between input features and label for the new examples. It is always said that less and clean data is better than large and messy data.



                2. If 49000 records added afterward are from the same set(i.e. have a good relationship between label and features) as that of 1000 before, there are again two possible reasons



                  A. If accuracy on the train dataset is small along with test dataset. e.g. training accuracy is 70% and test accuracy is 65%, then you are underfitting data. Model is very complex and dataset is small in terms of the number of examples.



                  B. If your training accuracy is near 100% and test accuracy is 65%, you are overfitting data. Model is complex, so you should go with some simple algorithm.



                  NOTE* Since you haven't mentioned about training accuracy, it is difficult to say what out of two above is happening.



                Now coming to your second question about real-world deployment. There is something called model staleness over time which is basically the problem of reducing accuracy of a model over time. https://medium.com/thelaunchpad/how-to-protect-your-machine-learning-product-from-time-adversaries-and-itself-ff07727d6712, this is the article by a product manager at Google how staleness problem and how it can be solved. This will answer your second question.



                Let me know if something is not clear.







                share|improve this answer








                New contributor




                Sagar Shelke is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.









                share|improve this answer



                share|improve this answer






                New contributor




                Sagar Shelke is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.









                answered 22 mins ago









                Sagar ShelkeSagar Shelke

                1




                1




                New contributor




                Sagar Shelke is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.





                New contributor





                Sagar Shelke is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.






                Sagar Shelke is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                Check out our Code of Conduct.



























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                    ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result